物理学报2025，Vol.74Issue(17)：1-9,9.DOI:10.7498/aps.74.20250652
面向软晶格筛选的立方钙钛矿体模量可解释性描述符研究
Descriptors for the interpretability of cubic perovskite bulk modulus oriented towards soft lattice screening
摘要
Abstract
In recent years,soft lattices have been considered a primary physical origin of defect tolerance in lead-halide perovskite materials,with bulk modulus serving as a key indicator of lattice"softness".This work focuses on cubic perovskites and constructing a dataset of bulk moduli for 213 compounds based on density functional theory(DFT)calculations.A total of 138 features are compiled,including 132 statistical features extracted using the Matminer toolkit and 6 manually selected elemental descriptors.Four conventional machine learning regression models(RF,SVR,KRR,and EXR)are employed for prediction.Of them,the SVR model shows the best performance,achieving a test-set Root Mean Square Error(RMSE)of 7.35 GPa and Coefficient of Determination(R2)of 97.86％.Feature importance analysis reveals that thermodynamic-structural features such as melting point,covalent radius,and atomic volume play dominant roles in determining bulk modulus.Based on the 12 most important features,a thermodynamic-structural coupling descriptor is constructed using the SISSO method,yielding a test-set RMSE of 7.41 GPa and R2 of 97.80％.The resulting descriptor indicates that the bulk modulus is proportional to melting point and inversely proportional to atomic volume.Furthermore,the VS-SISSO method combined with a random subset selection and iterative variable screening strategy is used,enabling the selection of electronic-level features such as electronegativity,valence state,and number of unpaired electrons.The resulting electronic-thermodynamic-structural coupling descriptor further improves the prediction accuracy,reaching an RMSE of 5.34 GPa and R2 of 98.35％on the test set.Notably,due to the difference in valence states,this model effectively distinguishes between the bulk moduli of chalcogen-based(divalent)and halogen-based(monovalent)perovskites.Based on this model,high-throughput screening is performed on over 10000 cubic chalcogenides and halide perovskites,and approximately 170 lead-free candidates with bulk moduli in the range of 10-20 GPa are identified,which are comparable to Pb-I perov-skites.These results provide preliminary evidence for supporting the applicability of the soft-lattice mechanism in lead-free systems and offer theoretical guidance and data support for the high-throughput discovery of stable,defect-tolerant,lead-free perovskite materials.All the data presented in this paper are openly available at https://doi.org/10.57760/sciencedb.j00213.00161.关键词
体模量/缺陷容忍性/软晶格/钙钛矿/可解释机器学习Key words
bulk modulus/defect tolerance/soft lattice/perovskite/interpretable machine learning引用本文复制引用
姜锦铭,孙庆德,张卫兵..面向软晶格筛选的立方钙钛矿体模量可解释性描述符研究[J].物理学报,2025,74(17):1-9,9.基金项目
国家自然科学基金(批准号:12474219)和湖南省自然科学基金(批准号:2023JJ40041)资助的课题. Project supported by the National Natural Science Foundation of China(Grant No.12474219)and the Natural Science Foundation of Hunan Province,China(Grant No.2023JJ40041). （批准号:12474219）
